This book started life in the Summer of 2008, when my employer, the University of Bristol, awarded me a one-year research fellowship. I decided to embark on writing a general introduction to machine learning, for two reasons. One was that there was scope for such a book, to complement the many more specialist texts that are available; the other was that through writing I would learn new things – after all, the best way to
learn is to teach.
The challenge facing anyone attempting to write an introductory machine learning text is to do justice to the incredible richness of the machine learning field without losing sight of its unifying principles. Put too much emphasis on the diversity of the discipline and you risk ending up with a ‘cookbook’ without much coherence; stress your favourite paradigm too much and you may leave out too much of the other interesting stuff. Partly through a process of trial and error, I arrived at the approach embodied in the book, which is is to emphasise both unity and diversity: unity by separate treatment of tasks and features, both of which are common across any machine learning approach but are often taken for granted; and diversity through coverage of a wide range of logical, geometric and probabilistic models.
Clearly, one cannot hope to cover all of machine learning to any reasonable depth within the confines of 400 pages. In the Epilogue I list some important areas for further study which I decided not to include. In my view, machine learning is a marriage of statistics and knowledge representation, and the subject matter of the book was chosen to reinforce that view. Thus, ample space has been reserved for tree and rule learning, before moving on to the more statistically-oriented material. Throughout the book I have placed particular emphasis on intuitions, hopefully amplified by a generous use of examples and graphical illustrations, many of which derive frommy work on the use of ROC analysis in machine learning.
